STTR Phase I: Real-Time Mineral Data Collection and Identification Module

这个小型企业技术转让第一阶段项目通过消除处理大体积样品来改善采矿和岩土钻井活动。岩心钻探每天消耗大约1万加仑的水，通常是在缺水地区。提出的新方法在钻井过程中不需要水，从而降低了对公共和野生动物饮用水源的污染风险。将典型钻井项目所需的时间从几周减少到几天，减少了所需能源密集型钻井设备的碳排放。该解决方案解决了传统技术获取地下数据的相关问题，其潜在细分市场价值为210亿美元。即将开发的新仪器将消除对非现场实验室服务、岩心钻探和现场地质学家的需求，从而降低行业生命周期成本，并刺激美国所需战略矿产的关键勘探活动。该项目的智力优势在于开发了一种新型钻井模块，该模块能够获取岩石和土壤的地下化学和岩土力学特性，从而快速准确地评估矿物的类型和价值。这些材料的传统评价方法是钻取岩心，这带来了成本、样品质量和物流等方面的问题。该研究结合了光学图像、x射线荧光和伽马射线密度，以及来自新管理数据库的抗压强度数据，该数据库将从开源地理空间数据、地质和岩土工程报告集合、岩心数据库、隐式地质建模原则和机器学习方法中开发，以表征岩石和土壤样品。它旨在解决目前阻碍此类工具进入行业的技术挑战；这些问题包括钻井侧壁的泥浆涂抹、目标环境的高压和高温，以及无线数据传输方面的挑战。本研究的目标是创建、验证和校准一种方法，通过硬件和软件创新的结合，对未知的岩石或土壤样品进行原位自动化表征。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This Small Business Technology Transfer Phase I project improves mining and geotechnical drilling activities by eliminating handling of bulky samples. Core drilling consumes about 10,000 gallons of water per day, often in water-deficient areas. The proposed new approach negates the need for water in the drilling process, thereby mitigating the risk of pollution to public and wildlife drinking water sources. Reducing the time required for a typical drilling project from weeks to days reduces the carbon emission from the energy-intensive drilling equipment required. This solution to problems associated with acquiring subsurface data with traditional techniques has an addressable market segment value of $21 billion. The new instrument to be developed will eliminate the need for off-site lab services, core drilling, and site geologists, resulting in lower industry life cycle costs and spurring critical exploration activities for needed US strategic minerals. The intellectual merit of this project is based in the development of a novel drilling module that will be able to acquire subsurface chemistry and geotechnical properties of rocks and soils, to assess the type and value of minerals present both quickly and accurately. The conventional way of evaluating these materials is by core drilling, which presents various problems related to cost, sample quality and logistics. This research uses a combination of optical images, x-ray fluorescence, and gamma-ray density, as well as compressive strength data from a newly curated database that will be developed from open-source geospatial data, collections of geology and geotechnical reports, rock core databases, implicit geology modeling principles, and machine learning methods to characterize rock and soil samples. It aims to solve the technical challenges that currently prevent such a tool from being available to the industry today; these include mud smearing in the drill hole sidewalls, the high pressure and heat of the target environment, and challenges in wireless data transmission. The goal of this research is to create, validate and calibrate a method for automating the characterization of an unknown rock or soil sample in situ via a combination of hardware and software innovations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.